The subject matter disclosed herein relates to three dimensional radiographic imaging and, in particular, to generating accurate volume images using iterative reconstruction algorithms during tomosynthesis and tomography image processing, such as in cone beam computed tomography (CBCT) systems using one or more radiation sources.
In radiographic image reconstruction, interior reconstruction, also known as limited field of view reconstruction, is a technique to correct truncation artifacts caused by limiting image data to a partial field of view. The reconstruction focuses on an area of the image referred to as the region of interest, which may typically be proximate to a boundary of the field of view. Interior reconstruction can be applied to various radiographic image types using one or more of various methods.
Cone beam reconstruction algorithms have been implemented in techniques such as filtered back projection algorithms. Approximate cone-beam reconstruction algorithms are important in the cases of incomplete scanning geometry and partial field of view coverage. Approximate reconstruction is usually associated with higher computational efficiency and less image artifacts such as noise and ringing. Adaptations have been made for cone beam reconstruction using circular scanning. Some existing cone-beam algorithms require that projections be complete at least along one direction and are therefore inapplicable in partial field of view cases where objects are larger than the cone beam field of view (cone beam angle) as defined by effective detection area and x-ray source position.
Continuous cone-beam projection measurements can be approximated as a set of digital image values captured at a 2D digital detector comprising a grid of photosensitive detector elements, each of which comprises a sum of weighted values of voxels exposed by, or proximate to, a corresponding path of an x-ray beam. A cone-beam imaging geometry is specified, including the distance between the source and the digital detector, the dimensions of the detector, as well as the distribution of the photosensitive elements over the receiving surface of the digital detector. An x-ray beam path may be computationally divided at equidistance points along its length equivalent to a voxel side length. The value of each dividing point may be an interpolation of the values of its nearest voxel neighbors. A 2D image projection datum associated with an x-ray beam path may be modeled as the sum of incremental attenuation contributions from all the dividing points (voxels) along the path.
Discrepancies between measured and estimated projection data may be computed, for example, as ratios or normalized differences for each combination of detector and source locations. These discrepancies (either ratios or differences) may be back projected over a stored 3D digital image. Known object image data may be combined to generate an updated image, and reconstruction errors may be estimated in image and/or projection domains. Decisions may be made to continue iterations or to output a reconstructed image for viewing at a workstation, for example, or any other suitable device or system, or for further processing.
In either reprojection or back projection, representative x-rays may be evenly divided with a pre-specified interval length (for example, the voxel side length as described herein) being consistent with the discrete cone-beam imaging model. In reprojection, the voxel values of eight nearest neighbors of each dividing point may contribute to the projection value using an interpolation technique. In back projection, a projection value may be additively redistributed to the eight nearest neighbors of each dividing point after weighting with corresponding interpolation coefficients.
In a scanning sequence for each x-ray projection image obtained, a field of view boundary is determined by the position and edges of the digital detector and the x-ray source location. During updating of voxels using a given projection, only voxels within the field of view will be updated, whereas the voxels outside the field of view maintain their previous values. Voxels may be updated at a greater frequency than adjacent or closely neighboring voxels. As a result, the number of times a given voxel is updated during the reconstruction process is dependent upon the location of the voxel in the reconstruction volume. The spatial variation of update counts per voxel generates artifacts in the final reconstructed volume image which typically occurs along spatial boundaries where the update counts between voxels shows a high discrepancy.
The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter.